OpenRaiser/Intern-Atlas huggingface dataset:
GerAPlanO - A new building design tool: design generation, thermal assessment and performance optimization
Venue: arXiv cs.HC 2015
Year: 2015

Building practitioners (architects, engineers, energy managers) are showing a growing interest in the design of more energy efficient and livable buildings. The best way to predict how a building will behave regarding energy consumption and thermal comfort is to use a dynamic simulation tool. However, the use of this kind of tools is difficult on a daily basis practice due to the heuristic and exploratory nature of the architectural design process. To deal with this difficulty, the University of Coimbra and three companies have been working on the development of a prototype design aiding tool, specifically devoted to the space planning phase of building design, under the project GerAPlanO (Automatic Generation of Architecture Floor plans with Energy Optimization). This project aims to combine the capabilities of design generation techniques, thermal assessment programs, and design optimization methods to provide assistance to decision makers. This paper presents the overall concept, as well as the current status of development of this tool.
The Emergence of Norms via Contextual Agreements in Open Societies
Venue: arXiv cs.MA 2015
Year: 2015

This paper explores the emergence of norms in agents' societies when agents play multiple -even incompatible- roles in their social contexts simultaneously, and have limited interaction ranges. Specifically, this article proposes two reinforcement learning methods for agents to compute agreements on strategies for using common resources to perform joint tasks. The computation of norms by considering agents' playing multiple roles in their social contexts has not been studied before. To make the problem even more realistic for open societies, we do not assume that agents share knowledge on their common resources. So, they have to compute semantic agreements towards performing their joint actions. %The paper reports on an empirical study of whether and how efficiently societies of agents converge to norms, exploring the proposed social learning processes w.r.t. different society sizes, and the ways agents are connected. The results reported are very encouraging, regarding the speed of the learning process as well as the convergence rate, even in quite complex settings.
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs
Venue: arXiv cs.MA 2015
Year: 2015

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.
Exponential Convergence Bounds using Integral Quadratic Constraints
Venue: arXiv cs.SY 2015
Year: 2015

The theory of integral quadratic constraints (IQCs) allows verification of stability and gain-bound properties of systems containing nonlinear or uncertain elements. Gain bounds often imply exponential stability, but it can be challenging to compute useful numerical bounds on the exponential decay rate. In this work, we present a modification of the classical IQC results of Megretski and Rantzer that leads to a tractable computational procedure for finding exponential rate certificates.
A Formal Study on Backward Compatible Dynamic Software Updates
Venue: arXiv cs.SE 2015
Year: 2015

We study the dynamic software update problem for programs interacting with an environment that is not necessarily updated. We argue that such updates should be backward compatible. We propose a general definition of backward compatibility and cases of backward compatible program update. Based on our detailed study of real world program evolution, we propose classes of backward compatible update for interactive programs, which are included at an average of 32% of all studied program changes. The definitions of update classes are parameterized by our novel framework of program equivalence, which generalizes existing results on program equivalence to non-terminating executions. Our study of backward compatible updates is based on a typed extension of W language.
Regularized Minimax Conditional Entropy for Crowdsourcing
Venue: arXiv cs.LG 2015
Year: 2015

There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item difficulty. We also propose an objective measurement principle, and show that our method is the only method which satisfies this objective measurement principle. We validate our method through a variety of real crowdsourcing datasets with binary, multiclass or ordinal labels.
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images
Venue: arXiv cs.CV 2015
Year: 2015

Edge preserving filters preserve the edges and its information while blurring an image. In other words they are used to smooth an image, while reducing the edge blurring effects across the edge like halos, phantom etc. They are nonlinear in nature. Examples are bilateral filter, anisotropic diffusion filter, guided filter, trilateral filter etc. Hence these family of filters are very useful in reducing the noise in an image making it very demanding in computer vision and computational photography applications like denoising, video abstraction, demosaicing, optical-flow estimation, stereo matching, tone mapping, style transfer, relighting etc. This paper provides a concrete introduction to edge preserving filters starting from the heat diffusion equation in olden to recent eras, an overview of its numerous applications, as well as mathematical analysis, various efficient and optimized ways of implementation and their interrelationships, keeping focus on preserving the boundaries, spikes and canyons in presence of noise. Furthermore it provides a realistic notion for efficient implementation with a research scope for hardware realization for further acceleration.
Coordination and Efficiency in Decentralized Collaboration
Venue: arXiv cs.SI 2015
Year: 2015

Environments for decentralized on-line collaboration are now widespread on the Web, underpinning open-source efforts, knowledge creation sites including Wikipedia, and other experiments in joint production. When a distributed group works together in such a setting, the mechanisms they use for coordination can play an important role in the effectiveness of the group's performance. Here we consider the trade-offs inherent in coordination in these on-line settings, balancing the benefits to collaboration with the cost in effort that could be spent in other ways. We consider two diverse domains that each contain a wide range of collaborations taking place simultaneously -- Wikipedia and GitHub -- allowing us to study how coordination varies across different projects. We analyze trade-offs in coordination along two main dimensions, finding similar effects in both our domains of study: first we show that, in aggregate, high-status projects on these sites manage the coordination trade-off at a different level than typical projects; and second, we show that projects use a different balance of coordination when they are "crowded," with relatively small size but many participants. We also develop a stylized theoretical model for the cost-benefit trade-off inherent in coordination and show that it qualitatively matches the trade-offs we observe between crowdedness and coordination.
Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion
Venue: arXiv cs.SI 2015
Year: 2015

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.
Mind the Gap
Venue: arXiv cs.DS 2015
Year: 2015

We examine the complexity of the online Dictionary Matching with One Gap Problem (DMOG) which is the following. Preprocess a dictionary $D$ of $d$ patterns, where each pattern contains a special gap symbol that can match any string, so that given a text that arrives online, a character at a time, we can report all of the patterns from $D$ that are suffixes of the text that has arrived so far, before the next character arrives. In more general versions the gap symbols are associated with bounds determining the possible lengths of matching strings. Finding efficient algorithmic solutions for (online) DMOG has proven to be a difficult algorithmic challenge. We demonstrate that the difficulty in obtaining efficient solutions for the DMOG problem even, in the offline setting, can be traced back to the infamous 3SUM conjecture. Interestingly, our reduction deviates from the known reduction paths that follow from 3SUM. In particular, most reductions from 3SUM go through the set-disjointness problem, which corresponds to the problem of preprocessing a graph to answer edge-triangles queries. We use a new path of reductions by considering the complementary, although structurally very different, vertex-triangles queries. Using this new path we show a conditional lower bound of $Ω(δ(G_D)+op)$ time per text character, where $G_D$ is a bipartite graph that captures the structure of $D$, $δ(G_D)$ is the degeneracy of this graph, and $op$ is the output size. We also provide matching upper-bounds (up to sub-polynomial factors) for the vertex-triangles problem, and then extend these techniques to the online DMOG problem. In particular, we introduce algorithms whose time cost depends linearly on $δ(G_D)$. Our algorithms make use of graph orientations, together with some additional techniques. Finally, when $δ(G_D)$ is large we are able to obtain even more efficient solutions.
Probabilistic Omnidirectional Path Loss Models for Millimeter-Wave Outdoor Communications
Venue: arXiv cs.IT 2015
Year: 2015

This letter presents a probabilistic omnidirectional millimeter-wave path loss model based on real-world 28 GHz and 73 GHz measurements collected in New York City. The probabilistic path loss approach uses a free space line-of-sight propagation model, and for non-line-of-sight conditions uses either a close-in free space reference distance path loss model or a floating-intercept path loss model. The probabilistic model employs a weighting function that specifies the line-of-sight probability for a given transmitter-receiver separation distance. Results show that the probabilistic path loss model offers virtually identical results whether one uses a non-line-of-sight close-in free space reference distance path loss model, with a reference distance of 1 meter, or a floating-intercept path loss model. This letter also shows that site-specific environmental information may be used to yield the probabilistic weighting function for choosing between line-of-sight and non-line-of-sight conditions.
Building the distributed WPS-services execution environment
Venue: arXiv cs.SE 2015
Year: 2015

The article describes the environment of WPS-based (Web Processing Service) distributed services, that uses scenarios in JavaScript programming language in order to integrate services with each other. The environment standardizes data processing procedures, stores all services-related information and offers the set of basic WPS-services.
Towards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation
Venue: arXiv cs.CV 2015
Year: 2015

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated \cite{danbnn}. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements.
Ringo: Interactive Graph Analytics on Big-Memory Machines
Venue: arXiv cs.DB 2015
Year: 2015

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.
Event-triggered control under time-varying rates and channel blackouts
Venue: arXiv cs.SY 2015
Year: 2015

This paper studies event-triggered stabilization of linear time-invariant systems over time-varying rate-limited communication channels. We explicitly account for the possibility of channel blackouts, i.e., intervals of time when the communication channel is unavailable for feedback. Assuming prior knowledge of the channel evolution, we study the data capacity, which is the maximum total number of bits that could be communicated over a given time interval, and provide an efficient real-time algorithm to lower bound it for a deterministic time-slotted model of channel evolution. Building on these results, we design an event-triggering strategy that guarantees Zeno-free, exponential stabilization at a desired convergence rate even in the presence of intermittent channel blackouts. The contributions are the notion of channel blackouts, the effective event-triggered control despite their occurrence, and the analysis and quantification of the data capacity for a class of time-varying continuous-time channels. Various simulations illustrate the results.
BitWhisper: Covert Signaling Channel between Air-Gapped Computers using Thermal Manipulations
Venue: arXiv cs.CR 2015
Year: 2015

It has been assumed that the physical separation (air-gap) of computers provides a reliable level of security, such that should two adjacent computers become compromised, the covert exchange of data between them would be impossible. In this paper, we demonstrate BitWhisper, a method of bridging the air-gap between adjacent compromised computers by using their heat emissions and built-in thermal sensors to create a covert communication channel. Our method is unique in two respects: it supports bidirectional communication, and it requires no additional dedicated peripheral hardware. We provide experimental results based on implementation of BitWhisper prototype, and examine the channel properties and limitations. Our experiments included different layouts, with computers positioned at varying distances from one another, and several sensor types and CPU configurations (e.g., Virtual Machines). We also discuss signal modulation and communication protocols, showing how BitWhisper can be used for the exchange of data between two computers in a close proximity (at distance of 0-40cm) at an effective rate of 1-8 bits per hour, a rate which makes it possible to infiltrate brief commands and exfiltrate small amount of data (e.g., passwords) over the covert channel.
iPrivacy: a Distributed Approach to Privacy on the Cloud
Venue: arXiv cs.CR 2015
Year: 2015

The increasing adoption of Cloud storage poses a number of privacy issues. Users wish to preserve full control over their sensitive data and cannot accept that it to be accessible by the remote storage provider. Previous research was made on techniques to protect data stored on untrusted servers; however we argue that the cloud architecture presents a number of open issues. To handle them, we present an approach where confidential data is stored in a highly distributed database, partly located on the cloud and partly on the clients. Data is shared in a secure manner using a simple grant-and-revoke permission of shared data and we have developed a system test implementation, using an in-memory RDBMS with row-level data encryption for fine-grained data access control
Vibration Control Design for Nonlinear Systems using Frequency Response Function
Venue: arXiv cs.SY 2015
Year: 2015

A nonlinear frequency response based adaptive vibration controller is proposed for a class of nonlinear mechanical systems. In order to obtain the nonlinear Frequency Response Function (FRF), the convergence properties of the system are studied by using the convergence (contraction) theory. If the system under consideration is: 1) convergent, it directly enables to derive a nonlinear FRF for a band of excitation inputs, 2) non-convergent, first a controller is used to obtain the convergence and then the corresponding FRF for a band of excitation inputs is derived. Now the gains of the proposed adaptive controller are tuned such that a desired closed-loop frequency response, in the presence of excitation inputs is achieved. Finally, a building structure with nonlinear cubic stiffness and a satellite system are considered to illustrate the theoretical results.
Context-Aware Wireless Small Cell Networks: How to Exploit User Information for Resource Allocation
Venue: arXiv cs.GT 2015
Year: 2015

In this paper, a novel context-aware approach for resource allocation in two-tier wireless small cell networks~(SCNs) is proposed. In particular, the SCN's users are divided into two types: frequent users, who are regular users of certain small cells, and occasional users, who are one-time or infrequent users of a particular small cell. Given such \emph{context} information, each small cell base station (SCBS) aims to maximize the overall performance provided to its frequent users, while ensuring that occasional users are also well serviced. We formulate the problem as a noncooperative game in which the SCBSs are the players. The strategy of each SCBS is to choose a proper power allocation so as to optimize a utility function that captures the tradeoff between the users' quality-of-service gains and the costs in terms of resource expenditures. We provide a sufficient condition for the existence and uniqueness of a pure strategy Nash equilibrium for the game, and we show that this condition is independent of the number of users in the network. Simulation results show that the proposed context-aware resource allocation game yields significant performance gains, in terms of the average utility per SCBS, compared to conventional techniques such as proportional fair allocation and sum-rate maximization.
A Secure Intelligent Decision Support System for Prescribing Medication
Venue: arXiv cs.CY 2015
Year: 2015

The process of electronic approach to writing and sending medical prescription promises to improve patient safety, health outcomes, maintaining patients privacy, promoting clinician acceptance and prescription security when compared with the customary paper method. Traditionally, medical prescriptions are typically handwritten or printed on paper and hand-delivered to pharmacists. Paper-based medical prescriptions are generating major concerns as the incidences of prescription errors have been increasing and causing minor to serious problems to patients, including deaths. In this paper, intelligent eprescription model that comprises a knowledge base of drug details and an inference engine that can help in decision making when writing a prescription was developed. The research implements the e-prescription model with multifactor authentication techniques which comprises password and biometric technology. Microsoft Visual Studio 2008, using C-Sharp programming language, and Microsoft SQL Server 2005 database were employed in developing the systems front end and back end respectively. This work implements a knowledge base to the e-prescription system which has added intelligence for validating doctors prescription and also added security feature to the e-prescription system.